{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Non linear least squares curve fitting"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") \n",
        "\n",
        "# yfinance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:20.790Z",
          "iopub.execute_input": "2022-05-22T20:09:20.795Z",
          "shell.execute_reply": "2022-05-22T20:09:21.259Z",
          "iopub.status.idle": "2022-05-22T20:09:21.239Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "symbol = 'AMD'\n",
        "\n",
        "start = '2021-01-01'\n",
        "end = '2022-01-01'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2021-01-04  92.110001  96.059998  90.919998  92.300003  92.300003  51802600\n2021-01-05  92.099998  93.209999  91.410004  92.769997  92.769997  34208000\n2021-01-06  91.620003  92.279999  89.459999  90.330002  90.330002  51911700\n2021-01-07  91.330002  95.510002  91.199997  95.160004  95.160004  42897200\n2021-01-08  95.980003  96.400002  93.269997  94.580002  94.580002  39816400",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-04</th>\n      <td>92.110001</td>\n      <td>96.059998</td>\n      <td>90.919998</td>\n      <td>92.300003</td>\n      <td>92.300003</td>\n      <td>51802600</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>92.099998</td>\n      <td>93.209999</td>\n      <td>91.410004</td>\n      <td>92.769997</td>\n      <td>92.769997</td>\n      <td>34208000</td>\n    </tr>\n    <tr>\n      <th>2021-01-06</th>\n      <td>91.620003</td>\n      <td>92.279999</td>\n      <td>89.459999</td>\n      <td>90.330002</td>\n      <td>90.330002</td>\n      <td>51911700</td>\n    </tr>\n    <tr>\n      <th>2021-01-07</th>\n      <td>91.330002</td>\n      <td>95.510002</td>\n      <td>91.199997</td>\n      <td>95.160004</td>\n      <td>95.160004</td>\n      <td>42897200</td>\n    </tr>\n    <tr>\n      <th>2021-01-08</th>\n      <td>95.980003</td>\n      <td>96.400002</td>\n      <td>93.269997</td>\n      <td>94.580002</td>\n      <td>94.580002</td>\n      <td>39816400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:21.246Z",
          "iopub.execute_input": "2022-05-22T20:09:21.250Z",
          "iopub.status.idle": "2022-05-22T20:09:21.951Z",
          "shell.execute_reply": "2022-05-22T20:09:22.008Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Returns'] = df['Adj Close'].pct_change() \n",
        "df = df.dropna()\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume  \\\nDate                                                                          \n2021-01-05  92.099998  93.209999  91.410004  92.769997  92.769997  34208000   \n2021-01-06  91.620003  92.279999  89.459999  90.330002  90.330002  51911700   \n2021-01-07  91.330002  95.510002  91.199997  95.160004  95.160004  42897200   \n2021-01-08  95.980003  96.400002  93.269997  94.580002  94.580002  39816400   \n2021-01-11  94.029999  99.230003  93.760002  97.250000  97.250000  48600200   \n\n             Returns  \nDate                  \n2021-01-05  0.005092  \n2021-01-06 -0.026302  \n2021-01-07  0.053471  \n2021-01-08 -0.006095  \n2021-01-11  0.028230  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-05</th>\n      <td>92.099998</td>\n      <td>93.209999</td>\n      <td>91.410004</td>\n      <td>92.769997</td>\n      <td>92.769997</td>\n      <td>34208000</td>\n      <td>0.005092</td>\n    </tr>\n    <tr>\n      <th>2021-01-06</th>\n      <td>91.620003</td>\n      <td>92.279999</td>\n      <td>89.459999</td>\n      <td>90.330002</td>\n      <td>90.330002</td>\n      <td>51911700</td>\n      <td>-0.026302</td>\n    </tr>\n    <tr>\n      <th>2021-01-07</th>\n      <td>91.330002</td>\n      <td>95.510002</td>\n      <td>91.199997</td>\n      <td>95.160004</td>\n      <td>95.160004</td>\n      <td>42897200</td>\n      <td>0.053471</td>\n    </tr>\n    <tr>\n      <th>2021-01-08</th>\n      <td>95.980003</td>\n      <td>96.400002</td>\n      <td>93.269997</td>\n      <td>94.580002</td>\n      <td>94.580002</td>\n      <td>39816400</td>\n      <td>-0.006095</td>\n    </tr>\n    <tr>\n      <th>2021-01-11</th>\n      <td>94.029999</td>\n      <td>99.230003</td>\n      <td>93.760002</td>\n      <td>97.250000</td>\n      <td>97.250000</td>\n      <td>48600200</td>\n      <td>0.028230</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:21.957Z",
          "iopub.execute_input": "2022-05-22T20:09:21.964Z",
          "iopub.status.idle": "2022-05-22T20:09:21.974Z",
          "shell.execute_reply": "2022-05-22T20:09:22.013Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(df['Adj Close'])\n",
        "plt.title(symbol + ' Stock Closing Price')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x576 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:21.982Z",
          "iopub.execute_input": "2022-05-22T20:09:21.986Z",
          "iopub.status.idle": "2022-05-22T20:09:22.087Z",
          "shell.execute_reply": "2022-05-22T20:09:22.183Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def model(t, coeffs):\n",
        "   return coeffs[0] + coeffs[1] * np.exp( - ((t-coeffs[2])/coeffs[3])**2 )"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.095Z",
          "iopub.execute_input": "2022-05-22T20:09:22.100Z",
          "iopub.status.idle": "2022-05-22T20:09:22.107Z",
          "shell.execute_reply": "2022-05-22T20:09:22.186Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.round(np.array(df['Adj Close']).squeeze(), 2)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.114Z",
          "iopub.execute_input": "2022-05-22T20:09:22.119Z",
          "iopub.status.idle": "2022-05-22T20:09:22.126Z",
          "shell.execute_reply": "2022-05-22T20:09:22.189Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "print([item for item, count in collections.Counter(x).items() if count > 1])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[79.06, 82.76, 78.55, 106.15]\n"
          ]
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.133Z",
          "iopub.execute_input": "2022-05-22T20:09:22.137Z",
          "iopub.status.idle": "2022-05-22T20:09:22.147Z",
          "shell.execute_reply": "2022-05-22T20:09:22.191Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x0 = np.array(x, dtype=float)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.154Z",
          "iopub.execute_input": "2022-05-22T20:09:22.159Z",
          "iopub.status.idle": "2022-05-22T20:09:22.167Z",
          "shell.execute_reply": "2022-05-22T20:09:22.195Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def residuals(coeffs, y, t):\n",
        "    return y - model(t, coeffs)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.173Z",
          "iopub.execute_input": "2022-05-22T20:09:22.178Z",
          "iopub.status.idle": "2022-05-22T20:09:22.253Z",
          "shell.execute_reply": "2022-05-22T20:09:22.198Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.optimize import leastsq\n",
        "\n",
        "t = np.arange(len(df['Adj Close']))\n",
        "x, flag = leastsq(residuals, x0, args=(df['Adj Close'], t))\n",
        "\n",
        "print(x)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 221.90203076 -142.48610387   72.3772972   207.85692932   97.25\n",
            "   95.36         91.78         90.79         88.21         89.45\n",
            "   88.75         91.53         92.79         94.13         94.71\n",
            "   88.84         87.52         85.64         87.66         88.86\n",
            "   87.89         87.84         87.9          91.47         90.91\n",
            "   92.35         92.66         93.77         91.46         89.94\n",
            "   88.64         89.58         85.37         84.74         86.94\n",
            "   82.42         84.51         86.39         84.13         80.86\n",
            "   77.75         78.52         73.96         78.53         77.52\n",
            "   81.23         81.05         82.5          82.75         82.63\n",
            "   78.12         79.06         80.3          78.38         76.48\n",
            "   76.22         77.41         77.14         76.           78.5\n",
            "   81.09         81.43         81.44         82.2          83.35\n",
            "   82.76         78.58         80.19         78.55         83.01\n",
            "   82.15         81.11         79.27         81.61         79.06\n",
            "   82.76         85.41         85.21         84.02         83.91\n",
            "   81.62         78.55         78.61         77.83         77.89\n",
            "   78.81         75.99         76.83         74.64         73.09\n",
            "   74.59         74.65         74.44         76.23         78.06\n",
            "   77.17         77.44         77.86         78.34         78.42\n",
            "   80.08         80.81         81.97         80.28         81.58\n",
            "   81.35         80.89         79.96         81.56         81.31\n",
            "   81.55         80.47         80.11         84.56         84.65\n",
            "   82.59         83.58         83.82         86.1          85.62\n",
            "   87.08         89.52         93.93         93.31         94.7\n",
            "   94.47         90.54         89.74         90.9          90.81\n",
            "   90.26         89.05         86.93         85.89         86.58\n",
            "   87.11         89.41         91.21         92.15         91.82\n",
            "   91.03         97.93        102.95        106.19        108.63\n",
            "  112.56        118.77        112.35        110.11        107.58\n",
            "  106.48        107.68        106.5         110.55        107.48\n",
            "  107.56        103.44        103.7         104.65        108.77\n",
            "  107.65        108.3         107.27        111.4         111.32\n",
            "  110.72        109.99        109.2         109.92        109.15\n",
            "  106.17        106.15        105.2         104.8         105.73\n",
            "  105.6         106.22        103.88        101.55        102.82\n",
            "  104.38        106.15        105.8         108.16        101.52\n",
            "  100.35        102.9         102.45        100.34        101.81\n",
            "  103.64        106.45        105.06        104.68        105.04\n",
            "  109.16        111.99        112.12        116.43        116.33\n",
            "  116.39        119.33        119.82        122.36        122.93\n",
            "  122.28        121.16        120.23        125.23        127.63\n",
            "  130.53        137.5         136.34        150.16        148.92\n",
            "  139.87        146.01        147.89        146.49        152.45\n",
            "  151.34        155.02        155.41        152.52        149.92\n",
            "  157.8         154.81        161.91        158.37        149.11\n",
            "  150.68        144.01        139.06        144.85        145.24\n",
            "  138.1         138.55        133.8         135.6         146.5\n",
            "  138.64        137.75        135.8         144.25        143.88\n",
            "  146.14        154.36        153.15        148.26        145.15\n",
            "  143.9       ]\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.259Z",
          "iopub.execute_input": "2022-05-22T20:09:22.263Z",
          "shell.execute_reply": "2022-05-22T20:09:22.446Z",
          "iopub.status.idle": "2022-05-22T20:09:22.438Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "plt.plot(t, df['Adj Close'], t, model(t, x))\n",
        "plt.title(symbol + ' Non Linear Least Squares Curve')\n",
        "plt.xlabel('Time [ns]')\n",
        "plt.ylabel('Adj Close [bins]')\n",
        "plt.legend(['Adj Close', 'Model'])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-05-22T20:09:22.456Z",
          "iopub.execute_input": "2022-05-22T20:09:22.461Z",
          "iopub.status.idle": "2022-05-22T20:09:22.546Z",
          "shell.execute_reply": "2022-05-22T20:09:22.612Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}